Abstract:
Using a combination of approaches including functional neuroimaging, neurophysiology, and theoretical physics, it is shown that decisions in the brain are taken by attractor networks in the cortex that are biased by the evidence for the decision. It is shown using integrate-and-fire neuronal networks that decision-making is inherently probabilistic because of noise caused by the random firing times of neurons in the brain (for a given mean rate). It is shown that confidence in a decision is an emergent property of the decision-making process, and human fMRI investigations that test this are described (Rolls 2010). It is argued that this mechanism for decision-making applies to decisions involving a choice between the emotional, implicit, evolutionarily old, brain systems, and the rational (reasoning) explicit conscious system that enables gene-specified goals to be deferred, in a decision between the phenotype and the selfish genotype (‘phenes’ vs genes) (Rolls 2005, 2008, 2010).

Abstract:
Circadian rhythms originate from feedback processes in genetic regulatory networks. Computational models of increasing complexity have been proposed for the molecular mechanism of these rhythms, which occur spontaneously with a period of about 24 h in all eukaryotes and some bacterial species. Whereas deterministic models for circadian rhythms in Drosophila account for a variety of dynamical properties, such as phase shifting by light pulses and entrainment by light-dark cycles, stochastic versions of these models allow us to examine how molecular noise affects the emergence and robustness of circadian oscillations. The dynamical bases of disorders of the sleep-wake cycle in humans will be addressed by means of a deterministic model for the mammalian circadian clock.